Amos, C. I., Wu, X., Broderick, P., Gorlov, I. P., Gu, J., Eisen, T. et al. Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat. Genet. 40, 616-622

Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA.
Nature Genetics (Impact Factor: 29.35). 06/2008; 40(5):616-22. DOI: 10.1038/ng.109
Source: PubMed

ABSTRACT To identify risk variants for lung cancer, we conducted a multistage genome-wide association study. In the discovery phase, we analyzed 315,450 tagging SNPs in 1,154 current and former (ever) smoking cases of European ancestry and 1,137 frequency-matched, ever-smoking controls from Houston, Texas. For replication, we evaluated the ten SNPs most significantly associated with lung cancer in an additional 711 cases and 632 controls from Texas and 2,013 cases and 3,062 controls from the UK. Two SNPs, rs1051730 and rs8034191, mapping to a region of strong linkage disequilibrium within 15q25.1 containing PSMA4 and the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5, were significantly associated with risk in both replication sets. Combined analysis yielded odds ratios of 1.32 (P < 1 x 10(-17)) for both SNPs. Haplotype analysis was consistent with there being a single risk variant in this region. We conclude that variation in a region of 15q25.1 containing nicotinic acetylcholine receptors genes contributes to lung cancer risk.

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Available from: Qiong Dong, Sep 27, 2015
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    • "Large-scale multinational genome-wide association studies (GWAS) of the genetic variation associated with lung cancer initially found that the 5p15.33, 6p21.33, and 15q25 regions were associated with risk of lung cancer among smokers (Amos et al., 2008; Hung et al., 2008). Interestingly, unique regions including 10q25.2, 6q22.2 and 6p21.32 were associated with lung cancer risk in those who had never smoked (Lan et al., 2012), suggesting that the risk variants for non-smoking related lung cancer were distinct from those for smoking related lung cancer. "
    05/2015; 123(6). DOI:10.1016/j.ebiom.2015.05.007
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    • "In Section 4, we first show the ability of the proposed methodology to detect relevant biomarkers using simulated data and also compare results to existing approaches. We then illustrate an application of the method to the lung cancer data of [2]. We conclude the paper with a brief discussion in Section 5. "
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    ABSTRACT: Complex diseases, such as cancer, arise from complex etiologies consisting of multiple single-nucleotide polymorphisms (SNPs), each contributing a small amount to the overall risk of disease. Thus, many researchers have gone beyond single-SNPs analysis methods, focusing instead on groups of SNPs, for example by analysing haplotypes. More recently, pathway-based methods have been proposed that use prior biological knowledge on gene function to achieve a more powerful analysis of genome-wide association studies (GWAS) data. In this paper we propose a novel Bayesian modeling framework to identify molecular biomarkers for disease prediction. Our method combines pathway-based approaches with multiple SNP analyses of a specified region of interest. The model's development is motivated by SNP data from a lung cancer study. In our approach we define gene-level scores based on SNP allele frequencies and use a linear modeling setting to study the scores association to the observed phenotype. The basic idea behind the definition of gene-level scores is to weigh the SNPs within the gene according to their rarity, based on genotype frequencies expected under the Hardy-Weinberg equilibrium law. This results in scores giving more importance to the unusually low frequencies, i.e. to SNPs that might indicate peculiar genetic differences between subjects belonging to different groups. An additional feature of our approach is that we incorporate information on SNP-to-SNP associations into the model. In particular, we use network priors that model the linkage disequilibrium between SNPs. For posterior inference, we design a stochastic search method that identifies significant biomarkers (genes and SNPs) for disease prediction. We assess performances on simulated data and compare results to existing approaches. We then show the ability of the proposed methodology to detect relevant genes and associated SNPs in a lung cancer dataset.
    Statistics and its interface 03/2015; 8(2):137-151. DOI:10.4310/SII.2015.v8.n2.a2 · 2.93 Impact Factor
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    • "These variants may have played a small role in the genetic etiology of weight throughout the history of our species, but may explain a larger proportion of the individual susceptibility to obesity in the modern environment of unrestricted access to processed food. A variety of other similar situations could be imagined, such as the interplay between addiction, tobacco use and lung cancer (Amos et al., 2008). In our simulations, we explore a range of parameter space in which the “modern” environment perturbs from 1 to 20% of the causal variants. "
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    ABSTRACT: The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05-2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits.
    Frontiers in Genetics 07/2014; 5:225. DOI:10.3389/fgene.2014.00225
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